Ian Lance Taylor writes:
> Ben_Tilly@trepp.com writes:
>
> > As before, humans get displaced. But now for every job a human can
> > learn, the same machines are able to learn more cheaply, and are better
> > for the employer than a human is. So people get displaced and stay
> > displaced. The worth of a human's work is now capped by effective
> > competition - the cost of buying a machine. As the price of that
> > machine falls, well you see why I call this a nightmare scenario...
>
> It's not a nightmare scenario. We just have to shift to a different
> economic system. Capitalism is not the only way to organize economic
> life--in fact, in human history, it's a relatively recent one.
All of the proposals I know about for switching away from capitalism for
a complex society either failed miserably or were never implemented. My
judgement of the ones not implemented is that they would have failed as
well. Betting on a successful replacement being found does not strike
me as wise.
> (In any case, I think your time estimates for robots which can do most
> things which humans can do are wildly optimistic. It's not merely a
> matter of raw computing power. I would guess that nanotechnology,
> which also presents problems for the economic system, is more likely
> to happen first.)
It may be wildly optimistic. Let me redo the estimate. (Rummages
Google.) OK, there appear to be 240 billion neurons in a human brain.
IIRC the relaxation time is 0.1 seconds, so there are 2.4e12 possible
firings per second. A modern Pentium's clock goes 2 billion times per
second. Assuming 20 cycles per operation (that is what it was when I
checked a while ago, I think it is a bit more now, but what the heck)
that is 1e8 potential operations per second. (Yeah, yeah, correct
away - I am just interested in orders of magnitude.) Moore's law comes
out to about a factor of 10 every 5 years. We need a factor of about
20,000. That takes, say, 20 years for 10,000 then 2 years more.
OK, inserting huge fudge factors it looks like computers that do a
similar number of "operations" to a human brain are due around 2024,
given massive assumptions and big error bars. If those operations are
remotely comparable (HAH!) then I would expect attempts at naive AI to
fail miserably until then - computers lack horsepower - and succeed
sometime after that point. But the operations are not comparable. So
let us say that it takes 1000 operations of a computer to simulate one
neuron firing. That pushes the due date for computational power out
to around 2040. However I have been estimating here for a $2000
machine to hit this point. A $200,000 cluster can hit that a decade
earlier, computational power might then be available by 2030. Of
course if a neuron is more complex it might take 100,000 operations
to simulate one human neuron. Push that back to 2040.
Those are some starting figures, take the envelope, scribble numbers to
your heart's content. Any reasonable way you play it I think you will
find that if the longest run of continual progress in the history of
the human race continues to the middle of this century, AI will be
feasible. If need be, by running a direct simulation of a human brain
in real time.
My advice to this list is that if we reach a point where it looks
feasible to hit what you consider appropriate computing power for
under 10 million, you have plenty of money, and Moore is going strong,
then I suggest going into the AI field. About a decade later it will be
an area with simply amazing growth potential.
> (The economics of the world in which robots can replace humans was
> satirized by Frederik Pohl in a series of stories collected in Midas
> World.)
>
People find it easy to satirize what they do not understand.
>
> I think one of the things this list struggles to investigate is the
> economics of abundance. Our current system of software licensing
> based on copyrights and patents imposes scarcity on what would
> otherwise be an abundant resource. It does this because some people
> think that is the best way to grow the resource; capitalism, which is
> the best way we know to grow resources, is based on managing scarcity.
> But the only true scarcity in the software world is people's time, and
> software licensing is not closely tied to that resource. If we
> eliminated the artificial restriction of software licensing, what
> would the economics look like? That is the world that FSBs live in.
My answer, in brief, to the FSB problem is to locate the bottlenecks
where potential abundance meets scarcity, plant yourself there, and
make yourself an efficient solver of that scarcity problem. But if
you (as many naively want to) find the abundance and position yourself
to stand in the middle of the abundance providing more, you may have
fun but you are likely to starve.
This is an ironically appropriate parallel.
> The discussion is somewhat warped by the fact that many people became
> very rich at Microsoft. So some FSBs look at that, and try to figure
> out how to also become very rich. But is it possible? Microsoft is
> the exception even in the artificial world of software licensing.
>
Karsten's post underscores exactly how much Microsoft is an exception.
Their profit margins are, of course, a wonderful demonstration of the
potential of monopoly dynamics. Unfortunately monopolies, by nature,
do not tend to share their abundance...
Cheers,
Ben